AMA, Agricultural Mechanization in Asia, Africa and Latin America
AMA, Agricultural Mechanization in Asia, Africa and Latin America (AMA) (issn: 00845841) is a peer reviewed journal first published online after indexing scopus in 1982. AMA is published by Farm Machinery Industrial Research Corp and Shin-Norinsha Co. AMA publishes every subjects of general engineering and agricultural engineering.
WOS Indexed (2026)
07 May 2026 (Vol - 57 , Issue- 05 )
31 May 2026 (Vol - 57 , Issue 05 )
Aim and Scope :
AMA, Agricultural Mechanization in Asia, Africa and Latin America
AMA, Agricultural Mechanization in Asia, Africa and Latin America (ISSN: 00845841) is a peer-reviewed journal. The journal covers Agricultural and Biological Sciences and all sort of engineering topic. the journal's scopes are in the following fields but not limited to:
Agricultural and Biological Sciences
Electrical Engineering and Telecommunication
Electronic Engineering
Computer Science & Engineering
Civil and architectural engineering
Mechanical and Materials Engineering
Transportation Engineering
Industrial Engineering
Industrial and Commercial Design
Information Engineering
Chemical Engineering
Food Engineering
Autonomous Detection of Cardia Ailments diagnosed by Electrocardiogram using various Supervised Machine Learning Algorithms
The electrocardiogram (ECG) signal is used to diagnose various Cardiac ailments as it holds the fundamental information to make appropriate decisions about different types of heart diseases. Hence several strategies were proposed to extract critical features from the ECG signal with highest accuracy which helps for the autonomous detection of Cardiac ailments. A methodology has been proposed in this work for state of the art in automatic detection of Cardiac ailments which include pre-processing, Feature extraction and Classification steps. A Butterworth third order band pass filter is used in pre-processing step and a four level Maximal overlap discrete wavelet packet transform (MODWPT) with symlet as mother wavelet is used for feature extraction step. Finally, for classification of considered three Cardiac ailments from MIT-BIH database i.e., Arrhythmia, Congestive Heart Failure and Atrial Fibrillation from Normal Sinus rhythm, five supervised Machine learning algorithms i.e., Support vector machine (SVM), K-nearest neighbour (KNN), Naive Bayes (NB), Decision tree (DT) and Random Forest (RF) were used which gives an overall accuracy of 90.83%, 90.56%, 90.28%, 91.39% and 91.94% for each classifier respectively. Clearly, random forest classifier for the proposed methodology gives better accuracy of the model for multiclass classification of cardiac ailments.
Optimization Study for Maximum Material Removal Speed when Grinding SKD11 tool steel with CBN Wheel on CNC Milling Machine
In this paper, the results of an optimization study when grinding SKD11 tool steel cylindrical parts with the use of CBN wheel and CNC milling machine. In the study, the maximum material removal speed (MRS) is the objective function. In addition, the influence of process parameters, including the spindle rotation speed, the depth of cut, the feed rate, and the wheel diameter on MRS was investigated. Moreover, optimal input parameters to achieve the maximum MRS have been proposed.
Comparison Between Analytical and Numerical Analysis on Blasting Pressure Relief of Tunnels
This paper uses analytical calculation analysis and numerical simulation research methods to study the pressure relief of tunnels. Blasting pressure relief achieves the purpose of improving surrounding rock support conditions. The high ground stress of the surrounding rock of the tunnel is transferred to the surrounding rock far away from the surface of the tunnel by loosening blasting to relieve the pressure so as to achieve the purpose of reducing the stress of the surrounding rock and protecting the tunnel. Finally, the simulation results of the internal force of the lining structure are found to be consistent with the analytical research and the numerical research results.
Optimization Study for Minimum Surface Roughness when Grinding with CBN Wheel on CNC Milling Machine
This paper presents the results of an optimization study when grinding SKD11 steel cylinder parts with CBN grinding wheels on CNC milling machines. In this study, the minimum surface roughness is chosen as the objective function. In addition, the influence of process parameters, including the spindel rotation speed, the depth of dressing cut, the feed rate, and the wheel diameter on the surface roughness was investigated. In addition, optimal input parameters to achieve minimum surface roughness were investigated.
Effect of Input Parameters on Material Removal Speed when EDM 90CrSi Steel with Graphite Electrodes
This paper introduces an optimization study when electrical discharge machining (EDM) cylindrical parts made of 90CrSi tool steel. The objective of this optimization study is to achieve the maximum material removal speed (MRS). This is an experimental study with the use of the Taguchi method in Minitab 19 software to design the experiment and analyze its results. The impact of the input process parameters, including the pulse on time, the pulse off time, the servo current, and the servo voltage on the MRS was evaluated. In addition, optimal EDM parameters for the maximum MRS were found.